Ultrasonic strain imaging is an emerging technique, which is likely to have numerous applications in the clinical examination of soft tissues. Here we are primarily (but not exclusively) interested in the subset of elasticity imaging techniques that are often grouped together as “static” (or “quasistatic”) strain imaging. In this paradigm, small tissue deformations are caused by contact with the ultrasound probe at the tissue surface; two or more ultrasound frames are recorded during this deformation, and some form of tracking is applied to the recorded ultrasound data to estimate tissue deformations, amounting to spatially-varying displacement fields. Spatial derivatives of such a displacement field are tissue strain, which indicates stiffness: there are sometimes further stages of analysis to estimate quantitative tissue properties directly, such as elastic moduli. Techniques of this kind were first tested clinically for breast scanning and breast screening has ever since been a key driver for research. Numerous studies have been motivated by prostate screening. Detection and staging of deep vein thrombosis also seems particularly promising, and there are many other possible applications.
It is helpful at this point to introduce some of the terminology generally used in ultrasound imaging. An ultrasound imaging system generally employs a one-dimensional or two-dimensional ultrasonic transducer array (although sometimes only a single transducer may be employed), the array comprising typically 20 to 256 transducers in each dimension. Each transducer acts as both a transmitter and a receiver. The transducers are generally driven by a pulse of RF energy, typically in the range 1-20 MHz; the signal may be considered narrow band in the sense that a pulse is sufficiently long to include a number of RF wavelengths thus having a relatively well-defined frequency. The ultrasound transducer array is usually coupled to the tissue under investigation by an ultrasound gel or water; typically the ultrasound penetrates a few centimetres, for example up to 25 cm, into the tissue under investigation, and the transducer array scans a region of a few centimetres in a lateral direction. The axial resolution is generally much greater than the lateral resolution, for example of the order of 1000 samples (in time) as compared with of the order of 100 lines laterally. So-called A-lines run actually from each transducer into the tissue under investigation; a so-called B-scan or B-mode image comprises a plane including a plurality of A-lines, thus defining a vertical cross section through the tissue. A B-scan is typically presented as a two-dimensional brightness image. A two-dimensional transducer array may be used to capture perpendicular B-scan images, for example to provide data for a three-dimensional volume.
A captured image is generally built-up by successively capturing data from along each of the A-lines in turn, that is by capturing a column of data centred on each ultrasonic transceiver in turn (although beam steering may be employed). However, when capturing data from a particular line, preferably a set of the transducers is driven, with gradually increasing phase away from the line on which the transducer is centred so as to create an approximately spherical ultrasonic wavefront converging on a focus on the line under investigation. The signals received from the transducers are summed with appropriate amplitude and phases to reconstruct the line data. This provides an RF (radio frequency) output which is usually time-gain compensated (because the amplitude of the received signal decreases with increasing probed depth) before being demodulated, optionally log-weighted and displayed as B-scan. Often the RF data is digitised at some point in the processing chain, for example prior to the demodulation, the remainder of the processing taking place in the digital domain. A pair of analogue-to-digital converters is typically employed to provide in-phase and quadrature digitised signal components so that phase data is available.
At least one-dimensional image data captured by a pulse-echo technique, in particular an ultrasonic imaging system, can be processed to determine deformation (displacement) data. The ultrasonic image data to be processed comprises digitised RF signal data, optionally with pre-processing in the analogue domain. Broadly speaking the demodulated data may be processed by envelope detection and log weighting to provide a B-mode display and/or strain determination may be employed to provide a strain display. The demodulation extracts the amplitude (envelope) and phase information of the RF signal in a conventional manner and the signal is digitised after demodulation so that the processed RF signal comprises a demodulated baseband signal; in other systems the RF signal may be digitised prior to demodulation.
A digitised I and Q (in-phase and quadrature) signal is frequently available in conventional ultrasonic imaging equipment and, conveniently, embodiments of the invention described later may be implemented by processing this signal using a suitably programmed general purpose computer or digital signal processor (DSP) and/or by using dedicated hardware.
Consider the task of estimating the deformation between a pair of RF ultrasound frames acquired pre- and post-deformation, when in general displacement is a continuously varying function of location. Displacement may be estimated by positioning a window over a small section of data in the pre-deformation frame and locating the closest matching window in the post-deformation frame. The displacement estimate is the difference between the pre- and post-deformation window positions.
The window matching approach to deformation estimation is sometimes problematic: pre- and post-deformation windows often match poorly, because deformation may not be negligible on the scale of the individual windows. Thus the post-deformation signals may be warped to increase the correlation between pre- and post-deformation windows, to implement an “adaptive” strain estimator. The simplest adaptive method is to apply a uniform stretch to the post-deformation signal, aiming to reverse part of the signal transformation that has actually taken place. Deformation data from adaptive strain estimators are measurably less noisy than standard displacement estimation, but the improvement is accompanied by a considerable increase in computational cost.
The task of window matching entails adjusting the post-deformation window position in order to find the optimum in a measure of signal similarity. One measure is the correlation coefficient, although similar performance may be obtained from techniques employing alternative measures such as the sum of squared differences (F. Viola and W. F. Walker, “A comparison of the performance of time-delay estimators in medical ultrasound”, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 50(4):392-401, April 2003) and the phase of the complex cross-correlation function (X. Chen, M. J. Zohdy, S. Y. Emelianov, and M. O'Donnell, “Lateral speckle tracking using synthetic lateral phase”, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 51(5):540-550, May 2004; M. O'Donnell, A. R. Skovoroda, B. M. Shapo, and S. Y. Emelianov, “Internal displacement and strain imaging using ultrasonic speckle tracking”, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 41:314-325, May 1994). The estimation procedure is repeated throughout a grid of locations, until the displacement field has been adequately sampled. We have recently noted, however, that window matching approaches can be enhanced: Since finite length windows are used to produce displacement estimates with low noise, the accuracy of the data can be improved by estimating the location at which the displacement estimate is valid. Implicitly assuming that the location is the window centre, results in an “amplitude modulation” artefact with the RF signal amplitude modulating the strain image. For this reason, we call our location estimation technique Amplitude Modulation Correction (AMC), and we have demonstrated that AMC yields better performance at lower computational cost than adaptive strain estimation (J. E. Lindop, G. M. Treece, A. H. Gee, and R. W. Prager, “Estimation of displacement location for enhanced strain imaging”, Technical Report CUED/F-INFENG/TR 550, Cambridge University Department of Engineering, March 2006). Further details of AMC can be found in our UK patent application no. 0606125.3 filed on 28 Mar. 2006 and also PCT/GB2007/050158 filed 27 Mar. 2007 hereby incorporated by reference in their entirety.
AMC can be implemented particularly easily in conjunction with phase-based displacement estimators and we have described a new family of highly versatile algorithms which we refer to as Weighted Phase Separation (WPS) in GB 0610172.9 filed 23 May 2006 and in PCT/GB2007/050163, filed 28 Mar. 2007, both hereby incorporated by reference in their entirety.
However, one of the engineering challenges in strain imaging is the development of a suitable clinical interface. Ultrasound clinicians have extensive experience with existing scanning modes including B-mode/greyscale, colour Doppler and power Doppler. Given the highly interactive nature of ultrasound examinations, the established modes have advantages in that clinicians are already well practised in the required scanning technique, they understand the significance of typical images, and they are generally familiar with its uses, benefits and disadvantages. The likelihood of an addition to the ultrasound tool-set gaining clinical favour may be boosted if it can be presented with an interface that: actively fosters the development of a successful scanning technique, by providing either visual or audio feedback; displays data in an intuitively meaningful format; and automatically guards against the presentation of misleading data.
Background prior art can be found in EPO 843 181A, which describes varying the dynamic range and noise rejection level of an ultrasonic image, and in U.S. Pat. No. 6,558,324 which describes displaying a colour-coded elasticity profile along with a B-mode display in a single, overlaid display.
The aforementioned issues concern how we present information. We may also consider what information to present. This raises at least two further issues. Qualitatively, what type of information can be provided (stiffness, strain, or an alternative compromise)? Quantitatively, how much data should be amalgamated to form each display image? The latter applies to many types of imaging systems, particularly those pertaining to time series data (where persistence may be helpful, whether in a real time display during acquisition or for post-processing) and to volumetric data (where spatial compounding can be applied to reduce noise).
Regarding the type of information, we note that ultrasonic strain imaging falls within a broader set of emerging elasticity imaging techniques. These are all essentially concerned with mechanical properties such as tissue stiffness, of which strain is only an indicator. Strain measurements can be converted into stiffness estimates if the stress field is known, but it is highly unlikely that this can be inferred from static (or quasistatic) deformation data without reducing the resolution and imposing limiting assumptions. Furthermore, such assumptions are unlikely to hold even approximately under in vivo scanning conditions, especially not with freehand scanning. On the other hand, strain images can in themselves sometimes be misleading, because an interpretation of low strain as indicating relatively high stiffness may be erroneous if the stress field varies substantially throughout the tissue. Some types of stress field variation occur repeatedly, and can hence be adjusted for. We will discuss the use of strain normalisation that varies both between images and within every individual image, so as to reduce the ambiguity of strain—we refer to the modified data after non-uniform normalisation as “pseudo-strain”.
In practice, an often more severe obstacle in freehand strain imaging is the basic challenge of achieving an acceptable strain estimation signal-to-noise ratio. Although many frames individually produce good images, typically a substantial fraction (and sometimes a majority) of frames may be difficult to interpret because of high estimation noise.